Dear friends
I have a modeled output from WRF for both past and future climate (the output is every 30 minutes for 30 years.) I also have past observed station data (every 30 minutes). I want to bias correct my modeled data from the observed data to remove biases in my modeled output. Can anyone suggest me which is the best method to use in order to achieve this ?
WRF (though the full form is deceiving) can simulate climate equally good sir as it encompasses almost all the necessary physics parameterization. There are many papers of WRF and climate research. We just need to set SST update properly. Along with certain other parameters in namelist.input (which takes care of long time regional climate runs). (ref: http://www2.mmm.ucar.edu/wrf/OnLineTutorial/Class_Sydney2014/Slides/20_ClimateRuns.pdf)
The problem is : I have grid output (from WRF) over Singapore for future climate simulations, that I wish to bring to station level. I want to bias correct the data using the past observation data. With the methods like quantile mapping e.t.c. Through this discussion I wanted to know are there any other methods to achieve my bias corrected outputs.
The model data output is something like reanalysis (20CR), the better thing to do I think would be merging the observation and model output.
There is no best method. But QM is the most widely applied method for impact studies. Simply, you can even use Delta Change approach. Please read following papers:
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., & Liebert, J. (2012). HESS Opinions" Should we apply bias correction to global and regional climate model data?". Hydrology and Earth System Sciences, 16(9), 3391-3404.
Haerter, J. O., Hagemann, S., Moseley, C., & Piani, C. (2011). Climate model bias correction and the role of timescales. Hydrology and Earth System Sciences, 15(3), 1065-1079.
Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1-2), 187-192.
Watanabe, S., Kanae, S., Seto, S., Yeh, P. J. F., Hirabayashi, Y., & Oki, T. (2012). Intercomparison of bias‐correction methods for monthly temperature and precipitation simulated by multiple climate models. Journal of Geophysical Research: Atmospheres, 117(D23).
Dear colleague,
Another general problem is related to stationarity/nonstationarity of the relationships one uses for present and future climate. Relationships identified under present climate could lose their relevance under future climate. Also, beside usefulness, one has to think about the physical coherence when modifying "biased" data which are the results of a physical framework, after all. Best wishes, Roxana.
Roxana, it is true the stationarity must exist for bias corrections methods. Since equation developed current climate is used to adjust future projections. Back to the question, the data you have are good data. I suggest you interpolate the gridded data from WRF to weather stations. Then do simple math WRF(forecast)-observation at station level' =bias. Then use the bias to adjust the WRF simulation for present and future simulations. I think no way out to avoid stationarity problem since we do not have future observations
Kenneth, We do not say future observations, future is future, we have future climate simulations. I think you understand we do not observe future climate, observed climate involve insitu measurements= raingauge,satellite, radar etc. All these are for nowcast not future. For future we need a model to predict
Generally we do not expect to have current observed climate or observed future climate from the climate models. The models gives climate simulations.
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